references.bib

@InCollection{gentle_monte_2005,
  title = {Monte {{Carlo Simulation}}},
  booktitle = {Encyclopedia of {{Statistics}} in {{Behavioral Science}}},
  author = {James E. Gentle},
  editor = {Brian S. Everitt and David C. Howell},
  year = {2005},
  pages = {1264--1271},
  publisher = {{American Cancer Society}},
  doi = {10.1002/0470013192.bsa412},
  abstract = {Monte Carlo simulation is one of the most important tools in all fields of science. Monte Carlo methods use samples of numbers that appear to be random, although the numbers are generated by deterministic computer algorithms. These ``pseudorandom'' samples can be used in solving a wide range of problems, including both deterministic problems such as evaluating a definite integral and problems in which there is a random component such as studying and comparing the performance of statistical procedures under various assumptions about the underlying distribution.},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/0470013192.bsa412},
  copyright = {Copyright \textcopyright{} 2005 John Wiley \& Sons, Ltd. All rights reserved.},
  file = {/home/brian/Zotero/storage/AC86NPR4/0470013192.html},
  isbn = {978-0-470-01319-9},
  keywords = {Monte Carlo simulation,Monte Carlo test,quadrature,variance reduction},
  language = {en},
}
@Article{park_random_1988,
  title = {Random Number Generators: Good Ones Are Hard to Find},
  shorttitle = {Random Number Generators},
  author = {S. K. Park and K. W. Miller},
  year = {1988},
  month = {oct},
  volume = {31},
  pages = {1192--1201},
  issn = {0001-0782},
  doi = {10.1145/63039.63042},
  abstract = {Practical and theoretical issues are presented concerning the design, implementation, and use of a good, minimal standard random number generator that will port to virtually all systems.},
  journal = {Communications of the ACM},
  number = {10},
}
@Article{harwell_strategy_2019,
  title = {A {{Strategy}} for {{Using Bias}} and {{RMSE}} as {{Outcomes}} in {{Monte Carlo Studies}} in {{Statistics}}},
  author = {Michael Harwell},
  year = {2019},
  month = {mar},
  volume = {17},
  pages = {jmasm.eP2938},
  issn = {1538-9472},
  doi = {10.22237/jmasm/1551907966},
  abstract = {To help ensure important patterns of bias and accuracy are detected in Monte Carlo studies in statistics this paper proposes conditioning bias and root mean square error (RMSE) measures on estimated Type I and Type II error rates. A small Monte Carlo study is used to illustrate this argument.},
  journal = {Journal of Modern Applied Statistical Methods},
  language = {en},
  number = {2},
}